FHWA Vehicle Classification Dataset

This study introduces a significant advancement in vehicle classification, addressing the challenge of limited annotated datasets compliant with Federal Highway Administration (FHWA) guidelines. We present a novel benchmark dataset meticulously curated from various sources to capture variations in time, resolution, camera position, and weather conditions. With a total of 17,174 annotated instances across 7,980 frames, this dataset offers a remarkable granularity for vehicle classification, making this study the first of its kind, to the best of the author’s knowledge. Our research also aims at detecting vehicle subcategories within the FHWA's classification scheme. Acknowledging the visual complexity in distinguishing vehicles with similar appearances but differing weights according to FHWA's criteria, we propose a refined classification system. This system categorizes vehicles into six subcategories based on axle count and spacing, facilitating easier and more precise classification. In addition, we proposed an improved YOLOv5 model that incorporates the Convolutional Block Attention Module (CBAM). The proposed model achieved performance scores of 0.981, 0.965, and 0.985 for precision, recall, and mAP_@50, respectively. As a result, the proposed model outperformed all previous YOLO iterations on the experimental test dataset. The addition of CBAM improves feature representation by focusing on important elements while ignoring irrelevant ones. The results show that the YOLOv5-CBAM integration is more precise and faster.

本研究在车辆分类领域取得了重大突破，针对符合美国联邦公路管理局（FHWA）指南的有限标注数据集的挑战进行了应对。我们提出了一种新颖的基准数据集，该数据集经过精心策划，汇集了多个来源的数据，以捕捉时间、分辨率、相机位置和天气条件的变化。该数据集包含17,174个标注实例，分布在7,980个帧中，为车辆分类提供了卓越的粒度，据作者所知，这是该领域内的首次尝试。我们的研究还旨在检测FHWA分类方案中的车辆子类别。鉴于根据FHWA标准，在视觉上区分外观相似但重量不同的车辆存在视觉复杂性，我们提出了一种精细的分类系统。该系统根据轴数和间距将车辆分为六个子类别，从而简化了分类过程并提高了精度。此外，我们还提出了一种改进的YOLOv5模型，该模型集成了卷积块注意力模块（CBAM）。所提出的模型在精确度、召回率和mAP_@50方面的性能得分分别为0.981、0.965和0.985。因此，所提出的模型在实验测试数据集上优于所有之前的YOLO版本。CBAM的加入通过关注重要元素并忽略无关元素，提高了特征表示。结果显示，YOLOv5-CBAM的集成在精度和速度方面都更为优越。